Here, we use the method “midastouch” - a predictive mean matching (PMM) method [1]}.
It works by initializing a feature's missing entries with its mean and then estimating them by training a model using the rest of the complete data.
For each prediction, a probability is assigned based on its distance from the value imputed for the desired entry.
The missing entry is imputed by randomly drawing from the observed values of the respective predictor, weighted according to the probability defined previously.
